README.md

---
license: mit
base_model:
- CodeGoat24/UnifiedReward-2.0-qwen-7b
---

# UnifiedReward-Edit-qwen-7B
[2025/10/23] 🔥🔥🔥 We release **UnifiedReward-Edit**-7b, a unified reward model for **both Text-to-Image and Image-to-Image generation**!!
For image editing reward task, our models support:

>1. Pairwise Rank — directly judge which of two edited images is better.
>
>2. Pairwise Score — assign a separate score to each image in a pair.
>
>3. Pointwise Score — rate a single image on two axes: instruction-following and overall image quality.

🚀 The image editing reward inference code is available at [`UnifiedReward-Edit/`](https://github.com/CodeGoat24/UnifiedReward/tree/main/UnifiedReward-Edit) directory, while T2I inference code is unchanged from previous models. The editing training data is preprocessed from [EditScore](https://huggingface.co/datasets/EditScore/EditScore-Reward-Data) and [EditReward](https://huggingface.co/datasets/TIGER-Lab/EditReward-Data) and will be released soon. We sincerely appreciate all contributors!!

For further details, please refer to the following resources:
- 📰 Paper: https://arxiv.org/pdf/2503.05236
- 🪐 Project Page: https://codegoat24.github.io/UnifiedReward/
- 🤗 Model Collections: https://huggingface.co/collections/CodeGoat24/unifiedreward-models-67c3008148c3a380d15ac63a
- 🤗 Dataset Collections: https://huggingface.co/collections/CodeGoat24/unifiedreward-training-data-67c300d4fd5eff00fa7f1ede
- 👋 Point of Contact: [Yibin Wang](https://codegoat24.github.io)


## Citation

```
@article{unifiedreward,
  title={Unified reward model for multimodal understanding and generation},
  author={Wang, Yibin and Zang, Yuhang and Li, Hao and Jin, Cheng and Wang, Jiaqi},
  journal={arXiv preprint arXiv:2503.05236},
  year={2025}
}
```